Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations72831
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 MiB
Average record size in memory192.0 B

Variable types

DateTime1
Categorical4
Numeric16
Boolean2

Alerts

Cloud3pm is highly overall correlated with SunshineHigh correlation
MaxTemp is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
MinTemp is highly overall correlated with MaxTemp and 2 other fieldsHigh correlation
Pressure3pm is highly overall correlated with Pressure9amHigh correlation
Pressure9am is highly overall correlated with Pressure3pmHigh correlation
Sunshine is highly overall correlated with Cloud3pmHigh correlation
Temp3pm is highly overall correlated with MaxTemp and 2 other fieldsHigh correlation
Temp9am is highly overall correlated with MaxTemp and 2 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed3pmHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
Rainfall has 50945 (69.9%) zeros Zeros
WindSpeed9am has 3018 (4.1%) zeros Zeros
Cloud9am has 3752 (5.2%) zeros Zeros
Cloud3pm has 2067 (2.8%) zeros Zeros

Reproduction

Analysis started2024-12-19 16:18:21.024032
Analysis finished2024-12-19 16:18:50.405574
Duration29.38 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Date
Date

Distinct3361
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2007-11-01 00:00:00
Maximum2017-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-19T18:18:50.482709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:50.666405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location
Categorical

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Brisbane
 
2434
Cairns
 
2381
Darwin
 
2289
GoldCoast
 
2276
Townsville
 
2245
Other values (44)
61206 

Length

Max length16
Median length11
Mean length8.7626972
Min length4

Characters and Unicode

Total characters638196
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Brisbane 2434
 
3.3%
Cairns 2381
 
3.3%
Darwin 2289
 
3.1%
GoldCoast 2276
 
3.1%
Townsville 2245
 
3.1%
Newcastle 2213
 
3.0%
CoffsHarbour 2197
 
3.0%
NorfolkIsland 2165
 
3.0%
Sydney 2105
 
2.9%
SalmonGums 1945
 
2.7%
Other values (39) 50581
69.4%

Length

2024-12-19T18:18:50.779071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brisbane 2434
 
3.3%
cairns 2381
 
3.3%
darwin 2289
 
3.1%
goldcoast 2276
 
3.1%
townsville 2245
 
3.1%
newcastle 2213
 
3.0%
coffsharbour 2197
 
3.0%
norfolkisland 2165
 
3.0%
sydney 2105
 
2.9%
salmongums 1945
 
2.7%
Other values (39) 50581
69.4%

Most occurring characters

ValueCountFrequency (%)
r 57341
 
9.0%
a 56790
 
8.9%
e 53988
 
8.5%
o 53508
 
8.4%
n 44866
 
7.0%
l 40908
 
6.4%
i 35869
 
5.6%
t 28345
 
4.4%
s 22643
 
3.5%
d 19634
 
3.1%
Other values (30) 224304
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 638196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 57341
 
9.0%
a 56790
 
8.9%
e 53988
 
8.5%
o 53508
 
8.4%
n 44866
 
7.0%
l 40908
 
6.4%
i 35869
 
5.6%
t 28345
 
4.4%
s 22643
 
3.5%
d 19634
 
3.1%
Other values (30) 224304
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 638196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 57341
 
9.0%
a 56790
 
8.9%
e 53988
 
8.5%
o 53508
 
8.4%
n 44866
 
7.0%
l 40908
 
6.4%
i 35869
 
5.6%
t 28345
 
4.4%
s 22643
 
3.5%
d 19634
 
3.1%
Other values (30) 224304
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 638196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 57341
 
9.0%
a 56790
 
8.9%
e 53988
 
8.5%
o 53508
 
8.4%
n 44866
 
7.0%
l 40908
 
6.4%
i 35869
 
5.6%
t 28345
 
4.4%
s 22643
 
3.5%
d 19634
 
3.1%
Other values (30) 224304
35.1%

MinTemp
Real number (ℝ)

High correlation 

Distinct317
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.926436
Minimum-3.6
Maximum29.1
Zeros6
Zeros (%)< 0.1%
Negative115
Negative (%)0.2%
Memory size1.1 MiB
2024-12-19T18:18:50.877753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.6
5-th percentile5.4
Q110.1
median13.7
Q317.8
95-th percentile23
Maximum29.1
Range32.7
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation5.3161439
Coefficient of variation (CV)0.38173039
Kurtosis-0.506853
Mean13.926436
Median Absolute Deviation (MAD)3.8
Skewness0.063170575
Sum1014276.3
Variance28.261386
MonotonicityNot monotonic
2024-12-19T18:18:50.986495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 576
 
0.8%
13.9 536
 
0.7%
13.2 536
 
0.7%
12 534
 
0.7%
13.4 520
 
0.7%
12.8 519
 
0.7%
12.5 519
 
0.7%
13.3 517
 
0.7%
13.5 515
 
0.7%
11 515
 
0.7%
Other values (307) 67544
92.7%
ValueCountFrequency (%)
-3.6 1
 
< 0.1%
-3.4 1
 
< 0.1%
-3.3 2
< 0.1%
-3.2 1
 
< 0.1%
-3.1 2
< 0.1%
-2.8 2
< 0.1%
-2.5 3
< 0.1%
-2.4 1
 
< 0.1%
-2.3 1
 
< 0.1%
-2.1 2
< 0.1%
ValueCountFrequency (%)
29.1 1
 
< 0.1%
28.6 1
 
< 0.1%
28.5 1
 
< 0.1%
28.3 3
< 0.1%
28.2 3
< 0.1%
28.1 1
 
< 0.1%
28 4
< 0.1%
27.9 4
< 0.1%
27.7 1
 
< 0.1%
27.6 3
< 0.1%

MaxTemp
Real number (ℝ)

High correlation 

Distinct317
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.145822
Minimum10.2
Maximum44.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:51.098040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile17.5
Q121
median24.8
Q328.9
95-th percentile34
Maximum44.9
Range34.7
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.1428218
Coefficient of variation (CV)0.20451993
Kurtosis-0.58947246
Mean25.145822
Median Absolute Deviation (MAD)3.9
Skewness0.31769373
Sum1831395.4
Variance26.448616
MonotonicityNot monotonic
2024-12-19T18:18:51.212609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 559
 
0.8%
22.2 536
 
0.7%
20 534
 
0.7%
21.2 522
 
0.7%
20.8 522
 
0.7%
23.4 522
 
0.7%
22 521
 
0.7%
24.8 515
 
0.7%
20.4 515
 
0.7%
22.5 511
 
0.7%
Other values (307) 67574
92.8%
ValueCountFrequency (%)
10.2 1
< 0.1%
10.4 1
< 0.1%
10.6 1
< 0.1%
10.8 1
< 0.1%
11 1
< 0.1%
11.1 2
< 0.1%
11.4 2
< 0.1%
11.7 1
< 0.1%
11.8 2
< 0.1%
11.9 2
< 0.1%
ValueCountFrequency (%)
44.9 1
 
< 0.1%
44.8 1
 
< 0.1%
44.6 1
 
< 0.1%
44.4 1
 
< 0.1%
44.1 1
 
< 0.1%
43.9 1
 
< 0.1%
43.6 1
 
< 0.1%
43.5 3
< 0.1%
43.1 1
 
< 0.1%
42.9 1
 
< 0.1%

Rainfall
Real number (ℝ)

Zeros 

Distinct424
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5650874
Minimum0
Maximum240
Zeros50945
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:51.326463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile8.8
Maximum240
Range240
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation6.0940669
Coefficient of variation (CV)3.8937551
Kurtosis179.88644
Mean1.5650874
Median Absolute Deviation (MAD)0
Skewness10.186843
Sum113986.88
Variance37.137651
MonotonicityNot monotonic
2024-12-19T18:18:51.435793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50945
69.9%
0.2 4005
 
5.5%
0.4 1827
 
2.5%
0.6 1188
 
1.6%
0.8 921
 
1.3%
2.349974074 820
 
1.1%
1 773
 
1.1%
1.2 687
 
0.9%
1.4 609
 
0.8%
1.6 513
 
0.7%
Other values (414) 10543
 
14.5%
ValueCountFrequency (%)
0 50945
69.9%
0.1 66
 
0.1%
0.2 4005
 
5.5%
0.3 22
 
< 0.1%
0.4 1827
 
2.5%
0.5 24
 
< 0.1%
0.6 1188
 
1.6%
0.7 4
 
< 0.1%
0.8 921
 
1.3%
0.9 8
 
< 0.1%
ValueCountFrequency (%)
240 1
< 0.1%
183.4 1
< 0.1%
182.6 1
< 0.1%
170.4 1
< 0.1%
165.2 1
< 0.1%
159.8 1
< 0.1%
157.8 1
< 0.1%
157.6 1
< 0.1%
156.8 1
< 0.1%
147.8 1
< 0.1%

Evaporation
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.365343
Minimum0
Maximum10
Zeros42
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:51.558582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.2
Q14.8
median5.4698242
Q35.6
95-th percentile8.4
Maximum10
Range10
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation1.7027512
Coefficient of variation (CV)0.31736111
Kurtosis0.90642716
Mean5.365343
Median Absolute Deviation (MAD)0.33017578
Skewness-0.11517675
Sum390763.3
Variance2.8993618
MonotonicityNot monotonic
2024-12-19T18:18:51.717345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.469824216 31986
43.9%
4 2109
 
2.9%
8 1896
 
2.6%
5 1259
 
1.7%
6 1225
 
1.7%
5.4 1172
 
1.6%
6.6 1149
 
1.6%
5.8 1148
 
1.6%
5.6 1142
 
1.6%
6.2 1128
 
1.5%
Other values (92) 28617
39.3%
ValueCountFrequency (%)
0 42
 
0.1%
0.1 2
 
< 0.1%
0.2 72
 
0.1%
0.3 4
 
< 0.1%
0.4 115
0.2%
0.5 3
 
< 0.1%
0.6 189
0.3%
0.7 6
 
< 0.1%
0.8 209
0.3%
0.9 4
 
< 0.1%
ValueCountFrequency (%)
10 390
0.5%
9.9 2
 
< 0.1%
9.8 330
0.5%
9.7 3
 
< 0.1%
9.6 381
0.5%
9.5 5
 
< 0.1%
9.4 384
0.5%
9.3 6
 
< 0.1%
9.2 439
0.6%
9.1 4
 
< 0.1%

Sunshine
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0049889
Minimum0
Maximum14
Zeros435
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:51.819092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17.6248531
median7.6248531
Q39.4
95-th percentile12.1
Maximum14
Range14
Interquartile range (IQR)1.7751469

Descriptive statistics

Standard deviation2.4457232
Coefficient of variation (CV)0.30552487
Kurtosis1.6358003
Mean8.0049889
Median Absolute Deviation (MAD)0.27514689
Skewness-0.61024456
Sum583011.35
Variance5.9815622
MonotonicityNot monotonic
2024-12-19T18:18:51.927685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.624853113 34722
47.7%
10.7 784
 
1.1%
10.8 727
 
1.0%
11 701
 
1.0%
10.5 690
 
0.9%
10.9 677
 
0.9%
10.6 648
 
0.9%
10.3 643
 
0.9%
11.1 639
 
0.9%
9.8 623
 
0.9%
Other values (132) 31977
43.9%
ValueCountFrequency (%)
0 435
0.6%
0.1 113
 
0.2%
0.2 136
 
0.2%
0.3 131
 
0.2%
0.4 101
 
0.1%
0.5 108
 
0.1%
0.6 91
 
0.1%
0.7 117
 
0.2%
0.8 92
 
0.1%
0.9 105
 
0.1%
ValueCountFrequency (%)
14 10
 
< 0.1%
13.9 11
 
< 0.1%
13.8 38
 
0.1%
13.7 43
 
0.1%
13.6 97
 
0.1%
13.5 85
 
0.1%
13.4 146
0.2%
13.3 174
0.2%
13.2 244
0.3%
13.1 269
0.4%

WindGustDir
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
W
10362 
SE
5366 
SSE
4890 
E
4854 
ENE
4565 
Other values (11)
42794 

Length

Max length3
Median length2
Mean length2.1127679
Min length1

Characters and Unicode

Total characters153875
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowW
5th rowWNW

Common Values

ValueCountFrequency (%)
W 10362
14.2%
SE 5366
 
7.4%
SSE 4890
 
6.7%
E 4854
 
6.7%
ENE 4565
 
6.3%
S 4469
 
6.1%
SW 4347
 
6.0%
WSW 4214
 
5.8%
NE 4173
 
5.7%
SSW 4139
 
5.7%
Other values (6) 21452
29.5%

Length

2024-12-19T18:18:52.042744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w 10362
14.2%
se 5366
 
7.4%
sse 4890
 
6.7%
e 4854
 
6.7%
ene 4565
 
6.3%
s 4469
 
6.1%
sw 4347
 
6.0%
wsw 4214
 
5.8%
ne 4173
 
5.7%
ssw 4139
 
5.7%
Other values (6) 21452
29.5%

Most occurring characters

ValueCountFrequency (%)
W 41404
26.9%
S 40337
26.2%
E 39620
25.7%
N 32514
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 41404
26.9%
S 40337
26.2%
E 39620
25.7%
N 32514
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 41404
26.9%
S 40337
26.2%
E 39620
25.7%
N 32514
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 41404
26.9%
S 40337
26.2%
E 39620
25.7%
N 32514
21.1%

WindGustSpeed
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.931142
Minimum9
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:52.146550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q131
median39
Q346
95-th percentile61
Maximum135
Range126
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.403924
Coefficient of variation (CV)0.28558973
Kurtosis1.9089976
Mean39.931142
Median Absolute Deviation (MAD)7
Skewness0.94807464
Sum2908225
Variance130.04949
MonotonicityNot monotonic
2024-12-19T18:18:52.259677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.98429166 5791
 
8.0%
35 5250
 
7.2%
39 4964
 
6.8%
31 4638
 
6.4%
37 4596
 
6.3%
33 4486
 
6.2%
41 4114
 
5.6%
30 3679
 
5.1%
43 3547
 
4.9%
28 3222
 
4.4%
Other values (49) 28544
39.2%
ValueCountFrequency (%)
9 5
 
< 0.1%
11 11
 
< 0.1%
13 59
 
0.1%
15 150
 
0.2%
17 271
 
0.4%
19 503
 
0.7%
20 823
 
1.1%
22 1042
1.4%
24 1792
2.5%
26 2249
3.1%
ValueCountFrequency (%)
135 1
 
< 0.1%
122 1
 
< 0.1%
120 1
 
< 0.1%
113 1
 
< 0.1%
107 4
 
< 0.1%
106 1
 
< 0.1%
104 2
 
< 0.1%
102 10
< 0.1%
100 5
< 0.1%
98 10
< 0.1%

WindDir9am
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
N
9067 
SE
5667 
SSE
5595 
S
4910 
E
4573 
Other values (11)
43019 

Length

Max length3
Median length2
Mean length2.1309058
Min length1

Characters and Unicode

Total characters155196
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowENE
5th rowW

Common Values

ValueCountFrequency (%)
N 9067
12.4%
SE 5667
 
7.8%
SSE 5595
 
7.7%
S 4910
 
6.7%
E 4573
 
6.3%
SW 4360
 
6.0%
NW 4359
 
6.0%
ESE 4201
 
5.8%
W 4019
 
5.5%
SSW 4012
 
5.5%
Other values (6) 22068
30.3%

Length

2024-12-19T18:18:52.373717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 9067
12.4%
se 5667
 
7.8%
sse 5595
 
7.7%
s 4910
 
6.7%
e 4573
 
6.3%
sw 4360
 
6.0%
nw 4359
 
6.0%
ese 4201
 
5.8%
w 4019
 
5.5%
ssw 4012
 
5.5%
Other values (6) 22068
30.3%

Most occurring characters

ValueCountFrequency (%)
S 41723
26.9%
N 39696
25.6%
E 39590
25.5%
W 34187
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 41723
26.9%
N 39696
25.6%
E 39590
25.5%
W 34187
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 41723
26.9%
N 39696
25.6%
E 39590
25.5%
W 34187
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 41723
26.9%
N 39696
25.6%
E 39590
25.5%
W 34187
22.0%

WindDir3pm
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
SE
8559 
NE
5129 
SSE
4797 
E
4737 
ENE
4706 
Other values (11)
44903 

Length

Max length3
Median length2
Mean length2.2059013
Min length1

Characters and Unicode

Total characters160658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowNW
5th rowW

Common Values

ValueCountFrequency (%)
SE 8559
 
11.8%
NE 5129
 
7.0%
SSE 4797
 
6.6%
E 4737
 
6.5%
ENE 4706
 
6.5%
ESE 4672
 
6.4%
S 4643
 
6.4%
W 4622
 
6.3%
SW 4409
 
6.1%
WSW 4370
 
6.0%
Other values (6) 22187
30.5%

Length

2024-12-19T18:18:52.503563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 8559
 
11.8%
ne 5129
 
7.0%
sse 4797
 
6.6%
e 4737
 
6.5%
ene 4706
 
6.5%
ese 4672
 
6.4%
s 4643
 
6.4%
w 4622
 
6.3%
sw 4409
 
6.1%
wsw 4370
 
6.0%
Other values (6) 22187
30.5%

Most occurring characters

ValueCountFrequency (%)
E 45433
28.3%
S 43285
26.9%
W 36617
22.8%
N 35323
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 45433
28.3%
S 43285
26.9%
W 36617
22.8%
N 35323
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 45433
28.3%
S 43285
26.9%
W 36617
22.8%
N 35323
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 45433
28.3%
S 43285
26.9%
W 36617
22.8%
N 35323
22.0%

WindSpeed9am
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.089066
Minimum0
Maximum57
Zeros3018
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:52.597078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q319
95-th percentile30
Maximum57
Range57
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.3775601
Coefficient of variation (CV)0.59461431
Kurtosis0.90723741
Mean14.089066
Median Absolute Deviation (MAD)6
Skewness0.73602792
Sum1026120.8
Variance70.183513
MonotonicityNot monotonic
2024-12-19T18:18:52.685922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
9 7177
 
9.9%
13 6926
 
9.5%
11 6117
 
8.4%
17 5728
 
7.9%
15 5626
 
7.7%
7 5465
 
7.5%
6 4619
 
6.3%
19 4597
 
6.3%
20 4139
 
5.7%
4 3377
 
4.6%
Other values (23) 19060
26.2%
ValueCountFrequency (%)
0 3018
4.1%
2 2156
 
3.0%
4 3377
4.6%
6 4619
6.3%
7 5465
7.5%
9 7177
9.9%
11 6117
8.4%
13 6926
9.5%
14.001988 882
 
1.2%
15 5626
7.7%
ValueCountFrequency (%)
57 6
 
< 0.1%
56 22
 
< 0.1%
54 11
 
< 0.1%
52 35
 
< 0.1%
50 35
 
< 0.1%
48 38
 
0.1%
46 88
0.1%
44 73
0.1%
43 99
0.1%
41 144
0.2%

WindSpeed3pm
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.348591
Minimum0
Maximum65
Zeros333
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:52.779323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q113
median19
Q324
95-th percentile33
Maximum65
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.3696137
Coefficient of variation (CV)0.43256968
Kurtosis0.60386707
Mean19.348591
Median Absolute Deviation (MAD)6
Skewness0.54019728
Sum1409177.2
Variance70.050434
MonotonicityNot monotonic
2024-12-19T18:18:52.878448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
17 6409
 
8.8%
20 6308
 
8.7%
19 5994
 
8.2%
13 5971
 
8.2%
15 5763
 
7.9%
24 4940
 
6.8%
22 4729
 
6.5%
11 4479
 
6.1%
9 4179
 
5.7%
28 3686
 
5.1%
Other values (27) 20373
28.0%
ValueCountFrequency (%)
0 333
 
0.5%
2 313
 
0.4%
4 904
 
1.2%
6 1476
 
2.0%
7 2369
 
3.3%
9 4179
5.7%
11 4479
6.1%
13 5971
8.2%
15 5763
7.9%
17 6409
8.8%
ValueCountFrequency (%)
65 8
 
< 0.1%
63 1
 
< 0.1%
61 10
 
< 0.1%
59 4
 
< 0.1%
57 13
 
< 0.1%
56 21
 
< 0.1%
54 28
 
< 0.1%
52 32
< 0.1%
50 73
0.1%
48 65
0.1%

Humidity9am
Real number (ℝ)

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.639528
Minimum30
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:52.978316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile43
Q157
median66
Q375
95-th percentile86
Maximum90
Range60
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.97843
Coefficient of variation (CV)0.19772278
Kurtosis-0.47261101
Mean65.639528
Median Absolute Deviation (MAD)9
Skewness-0.28285368
Sum4780592.5
Variance168.43964
MonotonicityNot monotonic
2024-12-19T18:18:53.083968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 2116
 
2.9%
68 2053
 
2.8%
67 2042
 
2.8%
69 2034
 
2.8%
66 2011
 
2.8%
64 1998
 
2.7%
70 1985
 
2.7%
63 1956
 
2.7%
71 1952
 
2.7%
62 1925
 
2.6%
Other values (52) 52759
72.4%
ValueCountFrequency (%)
30 112
 
0.2%
31 134
 
0.2%
32 151
0.2%
33 148
0.2%
34 199
0.3%
35 243
0.3%
36 268
0.4%
37 248
0.3%
38 328
0.5%
39 351
0.5%
ValueCountFrequency (%)
90 672
0.9%
89 823
1.1%
88 798
1.1%
87 911
1.3%
86 902
1.2%
85 1063
1.5%
84 1124
1.5%
83 1191
1.6%
82 1315
1.8%
81 1334
1.8%

Humidity3pm
Real number (ℝ)

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.784011
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:53.189027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q137
median50
Q360
95-th percentile74
Maximum80
Range60
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.00211
Coefficient of variation (CV)0.30752105
Kurtosis-0.8695459
Mean48.784011
Median Absolute Deviation (MAD)12
Skewness-0.0023303222
Sum3552988.3
Variance225.06331
MonotonicityNot monotonic
2024-12-19T18:18:53.294314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.48260609 2340
 
3.2%
52 1590
 
2.2%
51 1583
 
2.2%
57 1563
 
2.1%
55 1559
 
2.1%
53 1558
 
2.1%
54 1523
 
2.1%
56 1518
 
2.1%
59 1516
 
2.1%
50 1513
 
2.1%
Other values (52) 56568
77.7%
ValueCountFrequency (%)
20 680
0.9%
21 729
1.0%
22 798
1.1%
23 880
1.2%
24 870
1.2%
25 971
1.3%
26 1020
1.4%
27 1039
1.4%
28 1084
1.5%
29 1067
1.5%
ValueCountFrequency (%)
80 406
0.6%
79 454
0.6%
78 474
0.7%
77 485
0.7%
76 563
0.8%
75 594
0.8%
74 682
0.9%
73 644
0.9%
72 807
1.1%
71 859
1.2%

Pressure9am
Real number (ℝ)

High correlation 

Distinct252
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.5817
Minimum1000
Maximum1025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:53.406763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1007.8
Q11013.6
median1017.6538
Q31019.9
95-th percentile1023.7
Maximum1025
Range25
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7388154
Coefficient of variation (CV)0.0046615196
Kurtosis-0.044828625
Mean1016.5817
Median Absolute Deviation (MAD)3.1462416
Skewness-0.53043811
Sum74038660
Variance22.456372
MonotonicityNot monotonic
2024-12-19T18:18:53.519907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017.653758 7788
 
10.7%
1017.9 521
 
0.7%
1016.4 510
 
0.7%
1018 507
 
0.7%
1017.8 502
 
0.7%
1017.3 500
 
0.7%
1017.7 495
 
0.7%
1019.4 492
 
0.7%
1019.1 490
 
0.7%
1018.7 489
 
0.7%
Other values (242) 60537
83.1%
ValueCountFrequency (%)
1000 5
< 0.1%
1000.1 7
< 0.1%
1000.2 3
 
< 0.1%
1000.3 5
< 0.1%
1000.4 2
 
< 0.1%
1000.5 3
 
< 0.1%
1000.6 4
< 0.1%
1000.7 7
< 0.1%
1000.8 8
< 0.1%
1000.9 4
< 0.1%
ValueCountFrequency (%)
1025 279
0.4%
1024.9 268
0.4%
1024.8 242
0.3%
1024.7 242
0.3%
1024.6 276
0.4%
1024.5 253
0.3%
1024.4 296
0.4%
1024.3 271
0.4%
1024.2 288
0.4%
1024.1 268
0.4%

Pressure3pm
Real number (ℝ)

High correlation 

Distinct253
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.1048
Minimum1000
Maximum1025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:53.628056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1005.4
Q11011
median1015.2
Q31017.4
95-th percentile1021.3
Maximum1025
Range25
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation4.7575276
Coefficient of variation (CV)0.0046913571
Kurtosis-0.22141813
Mean1014.1048
Median Absolute Deviation (MAD)3.2
Skewness-0.41751561
Sum73858265
Variance22.634069
MonotonicityNot monotonic
2024-12-19T18:18:53.738193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.258204 7758
 
10.7%
1015.7 513
 
0.7%
1015.5 502
 
0.7%
1015.6 501
 
0.7%
1015.4 498
 
0.7%
1015.3 496
 
0.7%
1015.8 493
 
0.7%
1015.2 488
 
0.7%
1013.4 487
 
0.7%
1017.1 486
 
0.7%
Other values (243) 60609
83.2%
ValueCountFrequency (%)
1000 17
< 0.1%
1000.1 23
< 0.1%
1000.2 26
< 0.1%
1000.3 22
< 0.1%
1000.4 29
< 0.1%
1000.5 31
< 0.1%
1000.6 19
< 0.1%
1000.7 26
< 0.1%
1000.8 30
< 0.1%
1000.9 29
< 0.1%
ValueCountFrequency (%)
1025 8
 
< 0.1%
1024.9 8
 
< 0.1%
1024.8 15
< 0.1%
1024.7 9
 
< 0.1%
1024.6 19
< 0.1%
1024.5 20
< 0.1%
1024.4 27
< 0.1%
1024.3 27
< 0.1%
1024.2 33
< 0.1%
1024.1 22
< 0.1%

Cloud9am
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2896867
Minimum0
Maximum8
Zeros3752
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:53.824417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4.4371894
Q36
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1788913
Coefficient of variation (CV)0.50793716
Kurtosis-0.61984288
Mean4.2896867
Median Absolute Deviation (MAD)1.4371894
Skewness-0.25897529
Sum312422.17
Variance4.7475671
MonotonicityNot monotonic
2024-12-19T18:18:53.916704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.437189392 27819
38.2%
7 10018
 
13.8%
1 8798
 
12.1%
8 4797
 
6.6%
6 4554
 
6.3%
2 3901
 
5.4%
0 3752
 
5.2%
3 3411
 
4.7%
5 3201
 
4.4%
4 2580
 
3.5%
ValueCountFrequency (%)
0 3752
 
5.2%
1 8798
 
12.1%
2 3901
 
5.4%
3 3411
 
4.7%
4 2580
 
3.5%
4.437189392 27819
38.2%
5 3201
 
4.4%
6 4554
 
6.3%
7 10018
 
13.8%
8 4797
 
6.6%
ValueCountFrequency (%)
8 4797
 
6.6%
7 10018
 
13.8%
6 4554
 
6.3%
5 3201
 
4.4%
4.437189392 27819
38.2%
4 2580
 
3.5%
3 3411
 
4.7%
2 3901
 
5.4%
1 8798
 
12.1%
0 3752
 
5.2%

Cloud3pm
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3045771
Minimum0
Maximum8
Zeros2067
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:54.002467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4.5031669
Q35
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0095743
Coefficient of variation (CV)0.46684594
Kurtosis-0.40717762
Mean4.3045771
Median Absolute Deviation (MAD)0.5031669
Skewness-0.26188836
Sum313506.66
Variance4.038389
MonotonicityNot monotonic
2024-12-19T18:18:54.091649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4.5031669 29731
40.8%
1 8664
 
11.9%
7 8619
 
11.8%
6 4688
 
6.4%
2 4317
 
5.9%
3 4123
 
5.7%
5 3815
 
5.2%
8 3798
 
5.2%
4 3009
 
4.1%
0 2067
 
2.8%
ValueCountFrequency (%)
0 2067
 
2.8%
1 8664
 
11.9%
2 4317
 
5.9%
3 4123
 
5.7%
4 3009
 
4.1%
4.5031669 29731
40.8%
5 3815
 
5.2%
6 4688
 
6.4%
7 8619
 
11.8%
8 3798
 
5.2%
ValueCountFrequency (%)
8 3798
 
5.2%
7 8619
 
11.8%
6 4688
 
6.4%
5 3815
 
5.2%
4.5031669 29731
40.8%
4 3009
 
4.1%
3 4123
 
5.7%
2 4317
 
5.9%
1 8664
 
11.9%
0 2067
 
2.8%

Temp9am
Real number (ℝ)

High correlation 

Distinct202
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.985961
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:54.192912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.8
Q115.2
median18.5
Q322.4
95-th percentile27.7
Maximum30
Range20
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation4.7867786
Coefficient of variation (CV)0.25212201
Kurtosis-0.73062848
Mean18.985961
Median Absolute Deviation (MAD)3.6
Skewness0.28941882
Sum1382766.5
Variance22.913249
MonotonicityNot monotonic
2024-12-19T18:18:54.302634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 619
 
0.8%
16 591
 
0.8%
16.5 582
 
0.8%
16.6 581
 
0.8%
17.8 568
 
0.8%
15.2 565
 
0.8%
17.6 565
 
0.8%
17.2 562
 
0.8%
17.5 561
 
0.8%
15.4 550
 
0.8%
Other values (192) 67087
92.1%
ValueCountFrequency (%)
10 158
0.2%
10.1 143
0.2%
10.2 145
0.2%
10.3 140
0.2%
10.4 151
0.2%
10.5 171
0.2%
10.6 154
0.2%
10.7 168
0.2%
10.8 202
0.3%
10.9 175
0.2%
ValueCountFrequency (%)
30 129
0.2%
29.9 111
0.2%
29.8 115
0.2%
29.7 126
0.2%
29.6 119
0.2%
29.5 125
0.2%
29.4 145
0.2%
29.3 118
0.2%
29.2 139
0.2%
29.1 136
0.2%

Temp3pm
Real number (ℝ)

High correlation 

Distinct269
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.570073
Minimum15
Maximum43.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-12-19T18:18:54.411792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.3
Q119.7
median23
Q327
95-th percentile32.2
Maximum43.8
Range28.8
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation4.9031708
Coefficient of variation (CV)0.20802527
Kurtosis-0.47981511
Mean23.570073
Median Absolute Deviation (MAD)3.6
Skewness0.39932671
Sum1716632
Variance24.041084
MonotonicityNot monotonic
2024-12-19T18:18:54.964869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.68723497 1840
 
2.5%
20 575
 
0.8%
23 565
 
0.8%
19 545
 
0.7%
22.6 537
 
0.7%
21.9 534
 
0.7%
22 530
 
0.7%
21 529
 
0.7%
19.4 520
 
0.7%
21.5 518
 
0.7%
Other values (259) 66138
90.8%
ValueCountFrequency (%)
15 276
0.4%
15.1 227
0.3%
15.2 244
0.3%
15.3 229
0.3%
15.4 234
0.3%
15.5 242
0.3%
15.6 264
0.4%
15.7 237
0.3%
15.8 281
0.4%
15.9 273
0.4%
ValueCountFrequency (%)
43.8 1
 
< 0.1%
43.5 3
< 0.1%
42.8 1
 
< 0.1%
42.7 1
 
< 0.1%
42.5 1
 
< 0.1%
41.3 2
< 0.1%
41.2 1
 
< 0.1%
41.1 2
< 0.1%
40.9 1
 
< 0.1%
40.8 1
 
< 0.1%

RainToday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size640.1 KiB
False
60603 
True
12228 
ValueCountFrequency (%)
False 60603
83.2%
True 12228
 
16.8%
2024-12-19T18:18:55.062531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size640.1 KiB
False
60513 
True
12318 
ValueCountFrequency (%)
False 60513
83.1%
True 12318
 
16.9%
2024-12-19T18:18:55.142865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-12-19T18:18:48.411914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.416206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.856827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.423238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.977527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.344320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.061085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.465687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.815372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.421567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.811774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.365622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.200126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.712993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.128515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.579218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.494252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.511922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.078658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.507823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.064390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.462989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.155627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.560898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.896792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.506586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.899405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.467980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.311816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.803361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.223732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.667709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.588070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.602133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.168280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.595989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.149917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.558728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.251548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.654806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.981661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.594773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.004708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.584226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.432627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.899478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.312024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.755828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.669181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.687674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.253795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.676179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.230516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.644494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.337679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.734133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.070935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.687963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.087358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.688758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.552114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.980253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.397760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.840989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.755845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.773922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.336309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.760050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.312066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.721910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.426420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.813744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.155837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.766881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.172876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.782938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.637673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.059697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.476469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.957939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.837725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.865539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.428371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.846944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.400925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.801764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.519129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.897850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.248079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.851911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.263112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.891283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.753577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.144870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.574374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.045495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.922293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:25.964242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.517411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.932781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.490695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.886621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.609739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.977760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.341671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.936329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.368997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.018153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.839878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.228136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.676884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.128949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.997562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.052917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.598072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.020364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.571489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.965996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.694322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.055726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.420788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.019228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.463147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.096917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.925614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.322065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.765568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.217546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.080599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.140337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.688044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.109195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.660015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.051772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.773409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.134564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.509720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.103111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:41.183073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:44.405719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:47.304487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.161566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:32.132056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:43.113752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.494787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.950999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.394295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.244363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.317416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:27.868878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.283027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:30.830439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.215613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:33.937619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.295748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.894232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.275748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.762738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.359553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.205988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.579968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.035819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.481841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.333093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.401529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-19T18:18:32.295611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.022308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.376794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:36.974315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.390630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.858536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.449328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.290730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.659823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.121705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.564037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.423391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.487177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.051147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.451212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.001633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.380755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.118391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.463005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.062462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.475808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:39.939871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.531559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.372442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.741665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.203522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:47.645342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.515414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.580901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.140722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.708703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.094949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.483888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.202329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.554696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.159834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.560010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.037822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.619253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.460651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.840173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.300184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.143146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.603346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.671177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.237640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.797095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.178280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.599877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.289264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.642201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.248156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.645665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.135468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:41.994376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.546400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:44.937495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.396947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.235877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:49.695839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:26.766458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:28.331991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:29.890961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:31.266047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:32.967353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:34.379164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:35.735380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:37.336046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:38.729412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:40.236346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:42.099326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:43.634076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:45.031174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:46.495432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-19T18:18:48.326022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-19T18:18:55.220159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Cloud3pmCloud9amEvaporationHumidity3pmHumidity9amLocationMaxTempMinTempPressure3pmPressure9amRainTodayRainTomorrowRainfallSunshineTemp3pmTemp9amWindDir3pmWindDir9amWindGustDirWindGustSpeedWindSpeed3pmWindSpeed9am
Cloud3pm1.0000.460-0.0820.2580.1770.284-0.1130.059-0.055-0.1000.1570.2290.151-0.505-0.145-0.0290.0510.0440.0540.0760.0080.046
Cloud9am0.4601.000-0.0560.2650.2750.306-0.1270.131-0.036-0.0940.1760.1720.193-0.472-0.124-0.0310.0500.0500.0630.0640.0430.040
Evaporation-0.082-0.0561.000-0.101-0.2140.2670.3650.301-0.162-0.1570.1720.067-0.1700.1910.3440.3560.0650.0680.0660.0920.0890.061
Humidity3pm0.2580.265-0.1011.0000.4840.224-0.2040.3320.007-0.0630.2270.2840.270-0.269-0.2610.1790.0960.0580.070-0.0250.1250.005
Humidity9am0.1770.275-0.2140.4841.0000.121-0.1160.1030.004-0.0280.2930.1810.322-0.251-0.101-0.1110.0440.0460.042-0.144-0.088-0.226
Location0.2840.3060.2670.2240.1211.0000.2430.2740.2100.1990.1030.0970.0260.2780.2280.2650.2310.2070.2610.1620.1880.198
MaxTemp-0.113-0.1270.365-0.204-0.1160.2431.0000.642-0.371-0.2710.1510.079-0.1700.2130.9560.8140.1100.1140.120-0.041-0.025-0.108
MinTemp0.0590.1310.3010.3320.1030.2740.6421.000-0.327-0.3390.1380.1260.112-0.0010.6120.8680.1210.0900.1130.0720.1360.092
Pressure3pm-0.055-0.036-0.1620.0070.0040.210-0.371-0.3271.0000.9320.1150.191-0.0570.004-0.339-0.3580.1030.0750.090-0.201-0.128-0.030
Pressure9am-0.100-0.094-0.157-0.063-0.0280.199-0.271-0.3390.9321.0000.1700.181-0.1410.046-0.234-0.3260.0900.0720.081-0.254-0.173-0.091
RainToday0.1570.1760.1720.2270.2930.1030.1510.1380.1150.1701.0000.1790.2510.1570.1380.0640.1020.1450.0990.1130.0640.102
RainTomorrow0.2290.1720.0670.2840.1810.0970.0790.1260.1910.1810.1791.0000.0870.2560.0820.0690.1180.0990.1050.1860.0760.058
Rainfall0.1510.193-0.1700.2700.3220.026-0.1700.112-0.057-0.1410.2510.0871.000-0.176-0.164-0.0370.0080.0140.0060.1120.0640.103
Sunshine-0.505-0.4720.191-0.269-0.2510.2780.213-0.0010.0040.0460.1570.256-0.1761.0000.2310.1270.0690.0640.063-0.0450.0550.003
Temp3pm-0.145-0.1240.344-0.261-0.1010.2280.9560.612-0.339-0.2340.1380.082-0.1640.2311.0000.7730.1170.1150.122-0.073-0.049-0.111
Temp9am-0.029-0.0310.3560.179-0.1110.2650.8140.868-0.358-0.3260.0640.069-0.0370.1270.7731.0000.1280.0970.1230.0260.1250.017
WindDir3pm0.0510.0500.0650.0960.0440.2310.1100.1210.1030.0900.1020.1180.0080.0690.1170.1281.0000.1840.3380.0810.0670.067
WindDir9am0.0440.0500.0680.0580.0460.2070.1140.0900.0750.0720.1450.0990.0140.0640.1150.0970.1841.0000.2180.0590.0600.115
WindGustDir0.0540.0630.0660.0700.0420.2610.1200.1130.0900.0810.0990.1050.0060.0630.1220.1230.3380.2181.0000.1010.0650.073
WindGustSpeed0.0760.0640.092-0.025-0.1440.162-0.0410.072-0.201-0.2540.1130.1860.112-0.045-0.0730.0260.0810.0590.1011.0000.6170.498
WindSpeed3pm0.0080.0430.0890.125-0.0880.188-0.0250.136-0.128-0.1730.0640.0760.0640.055-0.0490.1250.0670.0600.0650.6171.0000.455
WindSpeed9am0.0460.0400.0610.005-0.2260.198-0.1080.092-0.030-0.0910.1020.0580.1030.003-0.1110.0170.0670.1150.0730.4980.4551.000

Missing values

2024-12-19T18:18:49.831093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T18:18:50.135716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6000005.4698247.624853W44.0WWNW20.00000024.071.022.01007.71007.18.0000004.50316716.921.8NoNo
12008-12-02Albury7.425.10.0000005.4698247.624853WNW44.0NNWWSW4.00000022.044.025.01010.61007.84.4371894.50316717.224.3NoNo
22008-12-03Albury12.925.70.0000005.4698247.624853WSW46.0WWSW19.00000026.038.030.01007.61008.74.4371892.00000021.023.2NoNo
42008-12-05Albury17.532.31.0000005.4698247.624853W41.0ENENW7.00000020.082.033.01010.81006.07.0000008.00000017.829.7NoNo
52008-12-06Albury14.629.70.2000005.4698247.624853WNW56.0WW19.00000024.055.023.01009.21005.44.4371894.50316720.628.9NoNo
92008-12-10Albury13.130.11.4000005.4698247.624853W28.0SSSE15.00000011.058.027.01007.01005.74.4371894.50316720.128.2YesNo
102008-12-11Albury13.430.40.0000005.4698247.624853N30.0SSEESE17.0000006.048.022.01011.81008.74.4371894.50316720.428.8NoYes
132008-12-14Albury12.621.03.6000005.4698247.624853SW44.0WSSW24.00000020.065.043.01001.21001.84.4371897.00000015.819.8YesNo
142008-12-16Albury9.827.72.3499745.4698247.624853WNW50.0NWNW14.00198822.050.028.01013.41010.30.0000004.50316717.326.2NoNo
162008-12-18Albury13.522.916.8000005.4698247.624853W63.0NWNW6.00000020.080.065.01005.81002.28.0000001.00000018.021.5YesYes
DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1421612017-05-24Uluru10.126.70.05.4698247.624853E41.0ESEE22.017.055.029.01022.61018.64.4371894.50316714.825.7NoNo
1421622017-05-25Uluru14.626.30.05.4698247.624853S37.0SSWS19.020.061.036.01022.01018.67.0000001.00000015.425.0NoNo
1421632017-05-26Uluru14.327.60.45.4698247.624853WNW39.0NNW0.020.068.027.01020.51016.14.0000007.00000016.127.2NoNo
1421652017-05-28Uluru8.024.60.05.4698247.624853E33.0SEESE11.013.046.025.01021.71018.84.0000004.50316713.823.5NoNo
1421662017-05-29Uluru12.722.20.05.4698247.624853E37.0EESE19.013.059.034.01024.31021.78.0000008.00000013.921.0NoNo
1421842017-06-16Uluru5.224.30.05.4698247.624853E24.0SEE11.011.053.024.01023.81020.04.4371894.50316712.323.3NoNo
1421892017-06-21Uluru2.823.40.05.4698247.624853E31.0SEENE13.011.051.024.01024.61020.34.4371894.50316710.122.4NoNo
1421902017-06-22Uluru3.625.30.05.4698247.624853NNW22.0SEN13.09.056.021.01023.51019.14.4371894.50316710.924.5NoNo
1421912017-06-23Uluru5.426.90.05.4698247.624853N37.0SEWNW9.09.053.024.01021.01016.84.4371894.50316712.526.1NoNo
1421922017-06-24Uluru7.827.00.05.4698247.624853SE28.0SSEN13.07.051.024.01019.41016.53.0000002.00000015.126.0NoNo